Onno Zoeter

Information retrieval system

A system and method are described for the access and retrieval of information, which integrates television, video and/or similar sources with the information resources available on the Internet. This invention permits a user to select an item displayed on a television screen and, without significant interruption, order the item or request additional information on the item or provide feedback to the television source signal provider, for example, the television network or advertiser.

Improved unscented kalman smoothing for stock volatility estimation

We introduce a novel approximate inference algorithm for nonlinear dynamical systems. The algorithm is based upon expectation propagation and Gaussian quadrature. The first forward pass is strongly related to the unscented Kalman filter. It improves upon unscented Kalman filtering by only making Gaussian approximations in the latent and not in the observation space. Smoothed estimates can be found without inverting latent space dynamics and can be improved by iteration. Multiple forward and backward passes make it possible to improve local approximations and make them as consistent as possible. We demonstrate the validity of the approach with an interesting inference problem in stochastic stock volatility models. The traditional unscented Kalman filter is ill suited for this problem: it can be proven that the traditional filter effectively never updates prior beliefs. The novel algorithm gives good results and improves with iteration

Deterministic approximate inference techniques for conditionally Gaussian state space models

We describe a novel deterministic approximate inference technique for conditionally Gaussian state space models, i.e. state space models where the latent state consists of both multinomial and Gaussian distributed variables. The method can be interpreted as a smoothing pass and iteration scheme symmetric to an assumed density filter. It improves upon previously proposed smoothing passes by not making more approximations than implied by the projection onto the chosen parametric form, the assumed density. Experimental results show that the novel scheme outperforms these alternative deterministic smoothing passes. Comparisons with sampling methods suggest that the performance does not degrade with longer sequences.

Split variational inference

We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are easier to approximate. In combination with mean-field approximations for each individual sub-part this leads to a tractable algorithm that alternates between the optimization of the bins and the approximation of the local integrals. We introduce suitable choices for the binning functions such that a standard mean field approximation can be extended to a split mean field approximation without the need for extra derivations. The method can be seen as a revival of the ideas underlying the mixture mean field approach. The latter can be obtained as a special case by taking soft-max functions for the binning.

Hierarchical visualization of time-series data using switching linear dynamical systems

We propose a novel visualization algorithm for high-dimensional time-series data. In contrast to most visualization techniques, we do not assume consecutive data points to be independent. The basic model is a linear dynamical system which can be seen as a dynamic extension of a probabilistic principal component model. A further extension to a particular switching linear dynamical system allows a representation of complex data onto multiple and even a hierarchy of plots. Using sensible approximations based on expectation propagation, the projections can be performed in essentially the same order of complexity as their static counterpart. We apply our method on a real-world data set with sensor readings from a paper machine.

Understanding Dynamic Pricing for Parking in Los Angeles: Survey and Ethnographic Results

The field of parking is going through a period of extreme innovation. Cities in the United States are now exploring new technology to improve on-street parking. One such innovation is dynamic pricing based on sensors and smart meters. This paper presents the results of two surveys and an ethnographic study in the context of LA Express ParkTM to understand users’ behaviors, knowledge and perceptions around parking. Survey results demonstrated that a high number of users misunderstood one of three tested stickers that convey time of day pricing. Furthermore, after discovering the availability of cheaper parking spots nearby, people expressed willingness to change their future behavior to park in those places. Ethnographic field studies found that it is common for many parkers to use handicapped placards for over eight hours in one parking session. A percentage of these parkers may be using placards illegally. We propose that increasing some parking restrictions during the day may curb placard use by making it more difficult to park for long periods.

New algorithms for parking demand management and a city-scale deployment

On-street parking, just as any publicly owned utility, is used inefficiently if access is free or priced very far from market rates. This paper introduces a novel demand management solution: using data from dedicated occupancy sensors an iteration scheme updates parking rates to better match demand. The new rates encourage parkers to avoid peak hours and peak locations and reduce congestion and underuse. The solution is deliberately simple so that it is easy to understand, easily seen to be fair and leads to parking policies that are easy to remember and act upon. We study the convergence properties of the iteration scheme and prove that it converges to a reasonable distribution for a very large class of models. The algorithm is in use to change parking rates in over 6000 spaces in downtown Los Angeles since June 2012 as part of the LA Express Park project. Initial results are encouraging with a reduction of congestion and underuse, while in more locations rates were decreased than increased.

Deterministic and Stochastic Gaussian Particle Smoothing

In this article we study inference problems in non-linear dynamical systems. In particular we are concerned with assumed density approaches to filtering and smoothing. In models with uncorrelated (but dependent) state and observation, the extended Kalman filter and the unscented Kalman filter break down. We show that the Gaussian particle filter and the one-step unscented Kalman filter make less assumptions and potentially form useful filters for this class of models. We construct a symmetric smoothing pass for both filters that does not require the dynamics to be invertible. We investigate the characteristics of the methods in an interesting problem from mathematical finance. Among others we find that smoothing helps, in particular for the deterministic one-step unscented Kalman filter.

Bayesian Generalized Linear Models in a Terabyte World

This paper introduces extremely fast approximate inference schemes for Bayesian treatments of dynamic generalized linear models. The approximations are tailored variants of quadrature EP. The first forward pass of this fixed point iteration algorithm can be interpreted as a one-step unscented Kalman filter. For on-line applications this filter can handle tens of thousands of updates a second on a current day desktop machine.

Multi-scale switching linear dynamical systems

Switching linear dynamic systems can monitor systems that operate in different regimes. In this article we introduce a class of multi-scale switching linear dynamical systems that are particularly suited if such regimes form a hierarchy. The setup consists of a specific switching linear dynamical system for every level of coarseness. Jeffreys rule of conditioning is used to coordinate the models at the different levels. When the models are appropriately constrained, inference at finer levels can be performed independently for every subtree. This makes it possible to determine the required degree of detail on-line. The refinements of very improbable regimes need not be explored. The computational complexity of exact inference in both the standard and the multi-class switching linear dynamical system is exponential in the number of observations. We describe an appropriate approximate inference algorithm based on expectation propagation and relate it to a variant of the Bethe free energy.